semi-intact forest = primary forest
secondary forest
brushy regrowth = savoka
agriculture = sugar cane, mixed agriculture, coffee, banana
agroforest = vanilla
flooded rice = rice
village

Merging similar land uses together:
1. semi-intact forest
2. agriculture = secondary_forest + brushy_regrowth + agriculture + agroforest
3. flooded_rice
4. village

1 Small-mammal exploration

Exploration of small mammals abundance and richness across land use

1.1 Abundance

1.2 Richness

1.3 Abundance by sp.

1.4 Common sp.

village host_species n
Andatsakala Mus musculus 83
Andatsakala Rattus rattus 235
Mandena Microgale brevicaudata 184
Mandena Mus musculus 86
Mandena Rattus rattus 345
Sarahandrano Mus musculus 81
Sarahandrano Rattus rattus 297

2 Microbiome data exploration

2.1 Filtering by abundance

Filtering ASVs by relative abundance
ASVs with relative reads count of less than x% within a sample are filtered out

2.1.1 Proportion left

Number of ASVs

t Microgale brevicaudata Mus musculus Rattus rattus
0.0 10655 5124 10353
0.1 3901 2888 7058
0.2 2172 2078 5316
0.3 1509 1648 4276
0.4 1136 1406 3593
0.5 937 1232 3085
1.0 518 739 1809
2.0 307 409 998

2.1.2 Proportion left by Grid

Number of ASVs

t grid Microgale brevicaudata Mus musculus Rattus rattus
0.0 semi-intact_forest 2366 NA 1748
0.0 agriculture 7164 4367 8959
0.0 flooded_rice 3740 1800 3844
0.0 village NA 1252 4897
0.1 semi-intact_forest 721 NA 1139
0.1 agriculture 2542 2467 6136
0.1 flooded_rice 1700 1210 2535
0.1 village NA 825 3308
0.2 semi-intact_forest 405 NA 750
0.2 agriculture 1437 1731 4609
0.2 flooded_rice 958 959 1841
0.2 village NA 642 2401
0.5 semi-intact_forest 196 NA 344
0.5 agriculture 641 1017 2669
0.5 flooded_rice 409 594 918
0.5 village NA 365 1341
1.0 semi-intact_forest 124 NA 168
1.0 agriculture 377 595 1525
1.0 flooded_rice 214 373 484
1.0 village NA 200 763

2.2

All ASVs with less than 0.01 relative abundance within a sample were filtered out

2.3 Reads distribution

2.3.1 Distribution

Total number of reads

[1] “mean: 28518.4992343032” [1] “median: 25516.5” [1] “range: 7” “range: 166634”

2.3.2 Threshold

How many individuals have less than X reads

reads_threshold n
0 1306
1000 1290
5000 1274
10000 1245
15000 1180
20000 982
25000 672
30000 431
35000 270
40000 187
45000 134
50000 104

2.3.3 Threshold by sp.

How many individuals have less than X reads

reads_threshold Microgale brevicaudata Mus musculus Rattus rattus
0 182 250 874
1000 173 248 869
5000 173 242 859
10000 172 232 841
15000 164 213 803
20000 130 177 675
25000 73 126 473
30000 33 92 306
35000 22 62 186
40000 16 55 116
45000 12 43 79
50000 7 41 56

2.3.4 Threshold by village

How many individuals have less than X reads

reads_threshold Andatsakala Mandena Sarahandrano
0 317 612 377
1000 315 599 376
5000 313 592 369
10000 305 580 360
15000 277 554 349
20000 220 431 331
25000 149 236 287
30000 96 110 225
35000 66 67 137
40000 53 48 86
45000 43 33 58
50000 38 23 43


2.4 ASVs Accumulation Curves

All host individuals with less than 1000 total reads were filtered out

2.4.1 Mus musculus

2.4.2 Rattus rattus

2.4.3 Microgale brevicaudata

2.5 ASVs Prevalence

2.5.1 Villages

In how many villages ASVs occur

2.5.2 Seasons

In how many seasons ASVs occur

2.5.3 Land uses

In how many grids ASVs occur

2.5.4 Land uses by village

In how many grids ASVs occur

2.5.5 Individuals

In how many individuals ASVs occur
Given by the proportion of individuals out of the total abundance of the focal host species

2.5.6 Individuals_2

In how many individuals ASVs occur

2.5.7 Individuals by grid

In how many individuals ASVs occur
Given by the proportion of individuals out of the total abundance of the focal host species in the grid

2.5.8 Host Species

In how many host species ASVs occur - shared ASVs

3 Microbes Alpha Diversity

3.1 Alpha Diversity

Calculating average (per host) alpha diversity measures
No normalization applied

3.1.1 Observed Richness

Average observed ASVs richness

3.1.2 Shannon Diversity

Average Shannon diversity

3.1.3 Phylogenetic Diversity

Average phylogenetic species evenness

3.2 GLM all variables

Independent variable: ASVs diversity (observed richness, shannon)
Dependent variables: village, grid, season, sex, mass
The best models: delta AICc <= 2

3.2.1 Mus musculus

ASVs richness -

(Intercept) grid mass season sex village df logLik AICc delta weight
17.81208 + NA NA NA NA 4 -812.2850 1632.735 0.0000000 0.140096881
20.37933 + -0.2178237 NA NA NA 5 -811.4301 1633.108 0.3734279 0.116235728
17.41129 NA NA NA NA NA 2 -815.0346 1634.118 1.3835373 0.070145071
18.62282 + NA NA NA + 6 -811.1458 1634.640 1.9055789 0.054030285
21.53217 + -0.2416901 NA NA + 7 -810.0989 1634.665 1.9299353 0.053376285
21.80403 + -0.2296774 + + + 10 -809.4687 1639.866 7.1310964 0.003962147
rowname Estimate Std. Error t value p
(Intercept) 21.804034 2.367732 9.208827 0.0000000
gridvillage -2.796999 1.354910 -2.064342 0.0400646

ASVs shannon -

(Intercept) grid mass season sex village df logLik AICc delta weight
1.467261 + NA NA NA + 6 -136.3021 284.9528 0.000000 0.313990806
1.437283 + NA NA + + 7 -135.9680 286.4027 1.449913 0.152080070
1.560507 NA NA NA NA NA 2 -143.5431 291.1351 6.182321 0.014270628
1.501380 + -0.0038554 + + + 10 -135.5505 292.0293 7.076557 0.009125613
rowname Estimate Std. Error t value p
(Intercept) 1.5013797 0.1563779 9.600969 0.0000000
villageMandena 0.1500372 0.0665795 2.253505 0.0251344
gridvillage 0.2871536 0.0894857 3.208934 0.0015147

ASVs Phylo -

(Intercept) grid mass season sex village df logLik AICc delta weight
0.1962168 + NA NA NA NA 4 260.8415 -513.5184 0.0000000 0.16061177
0.1659562 + 0.0025675 NA NA NA 5 261.5222 -512.7964 0.7219601 0.11194526
0.2042178 NA NA NA NA NA 2 258.1454 -512.2418 1.2765597 0.08483518
0.1968718 + NA + NA NA 6 261.9655 -511.5825 1.9358661 0.06101118
0.1616796 + 0.0021037 + + + 10 263.4562 -505.9841 7.5342620 0.00371307
rowname Estimate Std. Error t value p
(Intercept) 0.1616796 0.0312925 5.166726 0.0000005
gridvillage 0.0404207 0.0179068 2.257279 0.0248941

3.2.2 Rattus rattus

ASVs richness -

(Intercept) grid mass season sex village df logLik AICc delta weight
16.30833 NA -0.0130481 + NA + 7 -2714.545 5443.219 0.0000000 3.119794e-01
16.20379 NA -0.0145954 + + + 8 -2713.539 5443.245 0.0252732 3.080619e-01
15.20113 + -0.0152163 + + + 11 -2712.142 5446.592 3.3730104 5.776783e-02
15.69620 NA NA NA NA NA 2 -2729.631 5463.275 20.0556995 1.377483e-05
rowname Estimate Std. Error t value p
(Intercept) 15.2011321 1.4946388 10.170439 0.0000000
villageSarahandrano -1.2766860 0.5058941 -2.523623 0.0117946
mass -0.0152163 0.0053811 -2.827708 0.0047973
season3 1.4837053 0.4422159 3.355161 0.0008280

ASVs shannon -

(Intercept) grid mass season sex village df logLik AICc delta weight
1.312047 + NA NA + + 8 -507.2202 1030.608 0.0000000 1.982241e-01
1.340837 + NA + + + 10 -505.5083 1031.273 0.6651503 1.421415e-01
1.278259 + 0.0004579 NA + + 9 -506.6027 1031.415 0.8070416 1.324066e-01
1.279583 + NA NA NA + 7 -508.8161 1031.762 1.1545417 1.112886e-01
1.311781 + NA + NA + 9 -506.9437 1032.097 1.4890425 9.414883e-02
1.313407 + 0.0003713 + + + 11 -505.1215 1032.551 1.9432504 7.502136e-02
1.561372 NA NA NA NA NA 2 -524.6267 1053.267 22.6594349 2.380798e-06
rowname Estimate Std. Error t value p
(Intercept) 1.3134074 0.1179093 11.139138 0.0000000
villageMandena 0.1532312 0.0380378 4.028390 0.0000611
villageSarahandrano 0.1013163 0.0399090 2.538680 0.0113026
gridflooded_rice 0.2593851 0.1183649 2.191403 0.0286905

ASVs Phylo -

(Intercept) grid mass season sex village df logLik AICc delta weight
0.1885448 NA 0.0001073 NA NA + 5 975.7613 -1941.453 0.0000000 0.1885797402
0.1972880 NA NA NA NA + 4 974.6685 -1941.291 0.1623518 0.1738764609
0.1990704 NA NA + NA + 6 976.3535 -1940.610 0.8435815 0.1236840330
0.1921366 NA 0.0000841 + NA + 7 976.9995 -1939.869 1.5842374 0.0854047941
0.1889857 NA 0.0001121 NA + + 6 975.8151 -1939.533 1.9204504 0.0721895817
0.1979476 + 0.0000674 + + + 11 978.8909 -1935.474 5.9792803 0.0094866048
0.2116880 NA NA NA NA NA 2 965.3441 -1926.674 14.7787493 0.0001165012
rowname Estimate Std. Error t value p
(Intercept) 0.1979476 0.0213743 9.260995 0.0000000
villageSarahandrano 0.0257403 0.0072346 3.557929 0.0003943

3.2.3 Microgale brevicaudata

ASVs richness -

(Intercept) grid mass season sex df logLik AICc delta weight
9.781250 NA NA NA + 3 -527.9185 1061.979 0.000000 0.365162710
8.426021 NA 0.1510793 NA + 4 -527.6396 1063.517 1.538295 0.169219317
10.867052 NA NA NA NA 2 -530.1754 1064.421 2.442486 0.107673103
7.352543 + 0.1810116 + + 8 -527.0658 1071.010 9.030628 0.003994942
rowname Estimate Std. Error t value p
(Intercept) 7.352543 2.478996 2.965936 0.0034629

ASVs shannon -

(Intercept) grid mass season sex df logLik AICc delta weight
1.590274 NA NA NA NA 2 -74.61933 153.3092 0.000000 0.377978549
1.572888 NA NA NA + 3 -74.50860 155.1592 1.849974 0.149882310
1.639601 + -0.0024979 + + 8 -73.80826 164.4946 11.185328 0.001408005
rowname Estimate Std. Error t value p
(Intercept) 1.639601 0.1804806 9.08464 0

ASVs Phylo -

(Intercept) grid mass season sex df logLik AICc delta weight
0.2070646 NA NA NA NA 2 195.3066 -386.5425 0.000000 0.385942981
0.2237143 NA -0.0016738 NA NA 3 195.4751 -384.8081 1.734387 0.162145803
0.2346814 + -0.0019201 + + 8 196.0344 -375.1907 11.351855 0.001322815
rowname Estimate Std. Error t value p
(Intercept) 0.2346814 0.0379337 6.186621 0

3.3 GLMM land use

Fit a generalized linear mixed-effects model (GLMM) to check differences in ASVs alpha diversity between land uses
Fixed variable: grid
Random variables: village, season

Tukey comparisons:
1 = semi-intact forest
2 = secondary_forest
3 = brushy_regrowth
4 = agriculture
5 = agroforest
6 = flooded_rice
7 = village

3.3.1 Mus musculus

3.3.2 Rattus rattus

3.3.3 Microgale brevicaudata

4 Beta Diversity

4.1 Community Similarity - Population

Community similarity between land uses in the population level.
Population = aggregation of all individuals from the same grid.
In order to mitigate the bias in hosts abundance across grids, I randomly sample min number of individuals (min = no. of individuals in the smallest grid) from all the grids.
Then, I aggregate all the individuals from the same grid and calculate similarity between grids.
I repeat this 100 times and average the results.

4.1.1 Mus musculus

4.1.2 Rattus rattus

4.1.3 Microgale brevicaudata

4.2 Community Similarity - Distribution

4.2.1 Mus musculus

4.2.2 Rattus rattus

4.2.3 Microgale brevicaudata

4.3 PERMANOVA land use

4.3.1 Mus musculus

Jaccard

PERMANOVA PERMDIST
0.001 0.0024199
agriculture flooded_rice
flooded_rice 0.0015 NA
village 0.0015 0.002
p adj
flooded_rice-agriculture 0.4454326
village-agriculture 0.0016623
village-flooded_rice 0.0469547

Connectivity of distance matrix with threshold dissimilarity 1 Data are disconnected: 4 groups Groups sizes 1 2 3 4 245 1 1 1 Bray-Curtis

PERMANOVA PERMDIST
0.001 0.0293255
agriculture flooded_rice
flooded_rice 0.0015 NA
village 0.0015 0.012
p adj
flooded_rice-agriculture 0.9989345
village-agriculture 0.0272892
village-flooded_rice 0.0444988

Weighted UniFrac

PERMANOVA PERMDIST
0.999 0.8058829
agriculture flooded_rice
flooded_rice 0.997 NA
village 0.997 0.997
p adj
flooded_rice-agriculture 0.8787104
village-agriculture 0.9456352
village-flooded_rice 0.8117309

4.3.2 Rattus rattus

Jaccard

PERMANOVA PERMDIST
0.001 0
semi-intact_forest agriculture flooded_rice
agriculture 0.0080 NA NA
flooded_rice 0.0140 0.008 NA
village 0.0096 0.006 0.009
p adj
agriculture-semi-intact_forest 0.0000000
flooded_rice-semi-intact_forest 0.0000041
village-semi-intact_forest 0.0000006
flooded_rice-agriculture 0.2930703
village-agriculture 0.4296676
village-flooded_rice 0.9726514

Connectivity of distance matrix with threshold dissimilarity 1 Data are connected Bray-Curtis

PERMANOVA PERMDIST
0.001 0
semi-intact_forest agriculture flooded_rice
agriculture 0.0150 NA NA
flooded_rice 0.0420 0.049 NA
village 0.0255 0.006 0.0255
p adj
agriculture-semi-intact_forest 0.0000001
flooded_rice-semi-intact_forest 0.0000162
village-semi-intact_forest 0.0000334
flooded_rice-agriculture 0.3190588
village-agriculture 0.0198556
village-flooded_rice 0.9397812

Weighted UniFrac

PERMANOVA PERMDIST
1 0.1193888
semi-intact_forest agriculture flooded_rice
agriculture 1 NA NA
flooded_rice 1 1 NA
village 1 1 1
p adj
agriculture-semi-intact_forest 0.1492919
flooded_rice-semi-intact_forest 0.2061437
village-semi-intact_forest 0.3851565
flooded_rice-agriculture 0.9998780
village-agriculture 0.5612884
village-flooded_rice 0.8320159

4.3.3 Microgale brevicaudata

Jaccard

PERMANOVA PERMDIST
0.801 0.4726243
semi-intact_forest agriculture
agriculture 0.982 NA
flooded_rice 0.855 0.855
p adj
agriculture-semi-intact_forest 0.7578514
flooded_rice-semi-intact_forest 0.9881757
flooded_rice-agriculture 0.5014285

Connectivity of distance matrix with threshold dissimilarity 1 Data are connected Bray-Curtis

PERMANOVA PERMDIST
0.133 0.4744702
semi-intact_forest agriculture
agriculture 0.327 NA
flooded_rice 0.033 0.375
p adj
agriculture-semi-intact_forest 0.9967033
flooded_rice-semi-intact_forest 0.7257462
flooded_rice-agriculture 0.4476865

Weighted UniFrac

PERMANOVA PERMDIST
0.88 0.4652119
semi-intact_forest agriculture
agriculture 0.975 NA
flooded_rice 0.975 0.975
p adj
agriculture-semi-intact_forest 0.9455421
flooded_rice-semi-intact_forest 0.5517904
flooded_rice-agriculture 0.5092334